Solving the Cold Start Problem Using Product Related Contents
نویسنده
چکیده
To make rating predictions, one of the most commonly used approach in recommender systems is the latent factor model. Despite its popularity, one of its drawbacks is that it only makes use of numeric ratings but ignores other resources, such as review texts. McAuley et al. [1] proposed a general framework to employ both numeric ratings and review texts, and showed their model can outperform the latent factor model in various types of products. Although they found reviews to be a highly informative text source, reviews are not available in many recommendation settings, nor are they available for new users and items. To address this cold start problem, we based our work on the framework they developed, and experimented with other product related contents to replace review texts.
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